Research Briefs

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Using administrative databases to analyze a specific procedure or medical condition typically
involves using standardized classification schemes such as the International Classification of
Diseases, Version 9, Clinical Modification (ICD-9-CM). However, certain conditions may not be
accurately reflected by the ICD-9 codes. In fact, this study shows that the ICD-9 coding scheme
may be inaccurate in the classification of patients with ischemic cerebrovascular disease. The
researchers compared ICD-9 codes in an administrative database of patients hospitalized in five
academic medical centers in 1992 with clinical findings from the patients medical records. For
example, for ICD-9 code 434, 85 percent of patients were classified as having a stroke, and for
ICD-9 code 435, 77 percent had transient ischemic attacks. For code 436, 77 percent of patients
were classified as having strokes. According to the authors, the results from this study raise
doubts about the diagnostic accuracy of ICD-9 discharge codes for identifying patients with
ischemic cerebrovascular disease. Selection based on specific ICD-9 codes for this disease may be
affected by the number of discharge diagnoses used, the concomitant procedures performed
during the index admission, and most importantly by the actual clinical condition most likely
represented by the identifying code. When ICD-9 codes are the sole basis for patient selection
from administrative databases, these limitations may confound conclusions drawn concerning
outcomes, variations in health care delivery, and cost effectiveness of treatment, caution the
researchers.

When using administrative data, preexisting conditions (or comorbidities) are handled
analytically
by stratifying patients into groups—those with coexisting medical conditions and those
without;
employing separate binary indicators for discrete conditions; or summarizing comorbidity
information into an index or score that provides a single parameter
for measuring multiple comorbidities. Since comorbidities affect outcomes differently among
different patient groups, they probably should not be simplified as an index, conclude these
authors. They developed a comprehensive set of 30 comorbidity measures for use with
administrative inpatient databases to control for a broad array of patients underlying preexisting
conditions in many types of studies. The comorbidities were associated with substantial increases
in length of stay, hospital charges, and mortality both for heterogeneous and homogeneous
disease groups. The authors point out several comorbidities that are important predictors of
outcomes, yet are not commonly measured. These include: mental disorders, drug and alcohol
abuse, obesity, coagulopathy, weight loss, and fluid and electrolyte disorders. Reprints (AHCPR
Publication No. 98-R013) are available from the AHCPR Publications
Clearinghouse.

Cesarean delivery rates are one of the first measures used to judge hospital and health plan
performance. But to properly judge quality of care, cesarean delivery rates must be adjusted for
the mix of patients. Hospitals with more women with problem pregnancies would be expected to
have higher cesarean delivery rates. These researchers analyzed merged hospital and birth
certificate data for singleton births greater than 2,500 grams in Washington State in 1989 and
1990 to develop models to predict the probability that any given mother would have a cesarean
delivery.

They found that four factors—prior cesarean, breech but no prior cesarean, first birth, and
other
(factors)—explained 30 percent of the variance in individual cesarean rates. The full clinical
model
fit the data well and explained 37 percent of the variance. For instance, women who had delivered
previously and had no serious complications accounted for 35 percent of the mothers and
averaged less than 2 percent of cesareans. The authors conclude that adjustments for case mix
should not include all variables related to cesarean delivery rates, only the most predictive ones.
Proper adjustments may not alter hospital rankings greatly, but they will improve the validity and
acceptability of the reports.

A medical intervention's average cost and average effectiveness may be used to derive the
marginal cost-effectiveness ratio (MCER) for any two interventions. The MCER is defined as the
difference in average cost divided by the difference in average effectiveness. Estimating the
precision of MCERs is critical to clinical decisionmaking. These authors present a framework for
quantifying uncertainty about costs, effectiveness measures, and MCERs in complex decision
models. They discuss two alternative approaches, one based on Bayesian inference and the other
on resampling. While computationally intensive, these models are flexible in handling complex
distributional assumptions and a variety of outcome measures, according to the authors. They
conclude by extending their simplified models to a complex decision model using the
stroke-prevention policy model.

This article surveys categorical data methods widely applied in public health research.
Whereas
large sample chi-square methods, logistic regression analysis, and weighted least squares modeling
of repeated measures once were the primary analytic tools for categorical data problems, today's
methodology includes a much broader range of tools made available by increasing computational
efficiency. These include computational algorithms for exact inference of small samples and
sparsely distributed data and generalized estimating equations for cluster samples. The researchers
illustrate the various methods with examples, including a study of the prevalence of cerebral palsy
in very low birthweight infants and a study of cancer screening in primary care settings.

People with AIDS are susceptible to systemic fungal infections, a relapsing and
life-threatening
group of illnesses that increases in incidence as CD4 lymphocyte counts decrease. However, the
drug fluconazole is unlikely to be cost effective in preventing these infections unless its cost
($206) is lowered or it is focused on patients in regions with endemic fungal infections, conclude
these researchers. They used a Markov model with data from the research literature to project the
cost-effectiveness of fluconazole for prophylaxis against AIDS-related primary systemic fungal
infections in a hypothetical group of 100,000 AIDS patients. A no-prophylaxis policy was
associated with a discounted life expectancy of 28.2 months and direct medical costs of $36,100
per person. The strategy of treating patients with CD4 counts less than 200 increased costs to
$40,500 and life expectancy to 28.4 months, producing a cost-effectiveness ratio (CER) of
$240,000 per year of life saved (YLS). Compared with these two approaches, the intermediate
alternatives (fluconazole when CD4 counts reached less than 100 or less than 50) were less
economically efficient. A reduction in fluconazoles cost from $206 to $80 decreased the CER to
$50,000 for the under 200 CD4 strategy. Doubling fungal infection incidence lowered this ratio to
$96,000 per YLS.

These researchers describe the development of a classification system for medical
rehabilitation
patients that is based on the Functional Independence Measure (FIM), which is designed to
identify individuals likely to achieve similar motor FIM scores at discharge from rehabilitation.
The Discharge Motor FIM-Function Related Groups (DMF-FRGs) are part of the expanding
FIM-FRG system that includes an array of prediction tools for medical rehabilitation. The
researchers grouped 84,492 rehabilitation inpatients discharged in 1992 into 20 impairment
categories and then into FRGs by their admission motor FIM scores. Some FRGs were also
subdivided on the basis of admission cognitive FIM scores and age. The entire system explained
63 percent of the variation in motor FIM discharge scores in the validation data set. The
researchers conclude that clinicians can use the DMF-FRGs to identify groups of patients whose
motor FIM scores at discharge are below, within, or above nationally established ranges of values
for the purpose of outcomes management, guideline development, and quality improvement.

This paper describes development of a new version of the system to classify medical
rehabilitation
patients according to their use of rehabilitation resources. The system, the Functional
Independence Measure-Function Related Groups (FIM-FRGs), classifies patients into groups that
are similar with respect to rehabilitation length of stay. The researchers used data from 85,447
patient discharges in 1992 from 252 freestanding rehabilitation facilities and hospital units to
create FIM-FRGs Version 2.0. This version incorporates clinical and statistical criteria to increase
the percentage of patients classified, expand the impairment categories, and evaluate the
incremental predictability of coexisting medical diagnoses. This updated system explained 32
percent of the variance in length of stay in the 1992 validation sample and 31 percent in the 1990
discharges. The researchers conclude that FIM-FRGs Version 2.0 includes more specific
impairment categories, classifies a higher percentage of patient discharges, and appears
sufficiently stable over time to form the basis of a payment system for inpatient medical
rehabilitation.

The incidence of tuberculosis (TB) in this country surged 20 percent between 1985 and 1992
in
association with the AIDS epidemic. Monodrug resistance to TB has been as high as 33 percent
and multidrug-resistant TB (MDRTB) as high as 7 percent. In this study by researchers at the
MEDTEP Minority Research Center at Meharry Medical College, strategies are described that
could decrease and eventually eradicate the resurgent TB epidemic, which disproportionately
affects minorities. These involve public health policy measures for communities and health care
organizations and guidelines for health care providers. Most important is patient education about
the need to cover coughs and sneezes, which carry Mycobacterium tuberculosis, and to take all
prescribed medications to prevent TB disease, reinfection, or drug resistance. Education of health
care workers to better screen, diagnose, and treat TB is also needed. Another step is to place TB
patients on directly observed therapy (DOT), which has been shown to reduce the relapse rate and
the frequency of both primary and acquired drug resistance. If DOT is not feasible, the researchers
recommend monitoring patient compliance through interviews, pill counts, and urine tests.